from langchain.memory import ConversationBufferMemory

# Skipped here - Define your own prefix, suffix, and description with "chat_history" for the prompt
# Keep the original list of tools

# Create the prompt using ZeroShotAgent with additonal "chat_history" as input variables
agent_prompt = ZeroShotAgent.create_prompt(
    tools, prefix=prefix, suffix=suffix, input_variables=["input", "chat_history", "agent_scratchpad"],
)

# Create an instance of ZeroShotAgent with the LLMChain and the allowed tool names
zero_shot_agent = ZeroShotAgent(
    llm_chain=LLMChain(llm=llm, prompt=agent_prompt),
    allowed_tools=[tool.name for tool in tools]
)

# Initiate memory which allows for storing and extracting messages
memory = ConversationBufferMemory(memory_key="chat_history")

# Create an AgentExecutor with memory parameter
agent_chain = AgentExecutor.from_agent_and_tools(
    agent=zero_shot_agent, tools=tools, verbose=True, handle_parsing_errors=True, memory=memory
)

# Define initial question as user input
user_inquiry = "How do customers adapt their shopping habits during different seasons?"

# Run the agent to generate a response
agent_executor.run(user_inquiry)

# Define follow-up question as user input
user_inquiry = "Can you elaborate more?"

# Run the agent to generate another response
agent_executor.run(user_inquiry)